Examples of the Wisdom of Crowds in Daily Life

Recently I wrote an article for A List Apart, the site for people who make websites, on applying the Wisdom of Crowds to web communities (check out The Wisdom of Community). One of the interesting things about this stuff is, once you start seeing it, you realize it’s all around you. Here’s a list of places you can observe the wisdom of crowds in action. Note the common theme in all of them: many individual decisions that, when aggregated, say something more.

Do a Google search. The specifics of Google’s PageRank algorithm are a closely guarded secret, but the main idea is easy to grasp: the more sites that link to a certain URL with a certain phrase, the higher the rating. This works because each link is vote for the connection between the phrase and the site.

Go hiking. Note the trails. In most cases, they’re in good places, routing around natural hazards, toward locations of interest. This works because individual hikers blaze their own trails, but the effective ones get more use, which further defines the trail over time. This is exactly how ant lines work, except they use pheromones instead of visual trails in the dirt.

If you use iTunes, go to your music library and open the view preferences. Click the box to display the “Skip Count” of each song. This column will display the number of times you’ve clicked the “next song” button while that song was playing. In other words, how many times you’ve voted against that song. Sort the list by the Skip Count and you’ll likely find your least favorite songs. This works because your actions are selfishly motivated and the votes are passively recorded. In this case, the “crowd” is your actions over time.

If you use a file-sharing application like Limewire (for research purposes only, of course), try this: Open the app, limit the search to music if possible, and search for the name of an artist or band that you’ve heard of but don’t know well. Then sort the results by how many copies of each song the system found. This will be a great representation of the “best” songs from that band. This works because of how file-sharing networks work: individuals make their music libraries available to the network. If a song is popular, more people will have it. If it sucks, more people will delete it. Again, each user is making a selfish decision (“I never want to hear that again”), but the aggregate creates a ranking that’s more accurate than most expert reviews.

Go shopping at Amazon / rent movies from Netflix / record shows on your TiVo. All of these systems monitor your usage and compare you to other users on the network. If you bought/rented/recorded item X, and others who did that also enjoyed item Y, there’s a good chance you will too. This works because, again, it’s aggregating selfish behavior across a network. In this case, it’s comparing two data sets, which is tricky. But the results work more often than they do not.